An important and fundamental problem in various video processing applications, such as compression and zooming, is decimation and prediction. Decimation involves generating a smaller version of a source data. A common example is video compression, which uses a decimation filter to decimate an original frame in order to create a decimated frame. To decompress the video frame, a prediction filter generates a target frame from the decimated frame. The quality of the target frame may be expressed by measuring the difference between the original frame and the target frame, which is the prediction error. One existing process used to improve the quality of the target frame is to use an adaptive prediction filter. An adaptive prediction filter is modified with respect to a corresponding decimated frame to improve the quality of the target frame, while the decimation filter remains fixed. A classified prediction filter is a adaptive prediction filter that applies different filters/classes to different pixels/regions in an image.
FIG. 2A illustrates a fixed decimation filter with a decimation factor of ¼ that reduces the original frame to a decimated frame one quarter the size of the original. Original pixels 205 (unfilled) are each assigned a decimation filter coefficient of ¼. In other words, to generate the decimated pixel 210, original pixels 205 will be averaged. FIG. 2B illustrates a fixed decimation filter with a decimation factor of 1/9. Original pixels 250 (unfilled, plus an original pixel in the same location as target pixel 255) each contribute 1/9 to the value of decimated pixel 255. After applying the decimation filter illustrated in FIG. 2A or 2B, the source data has been reduced in size consistent with the decimation factor.